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Grid-Search Enhanced Tree-Based Machine Learning for Traffic IoT Data Anomaly Detection
University of Jinan, CHN.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-5824-425X
University of Jinan, CHN.
University of Jinan, CHN.
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2022 (English)In: Proceedings of the 12th International Conference on Computer Engineering and Networks / [ed] Liu Q., Liu X., Cheng J., Shen T., Tian Y., Springer Science+Business Media B.V., 2022, p. 3-9Conference paper, Published paper (Refereed)
Abstract [en]

Anomaly detection is an important part of machine learning. Detection of outliers in the field of transportation can provide valid data for future traffic predictions or traffic flow analysis. This paper builds a model based on XGBoost to detect outliers in IoT data. The data is preprocessed first, followed by model building. Then we use the grid search to adjust the parameters and substitute the optimal parameters into the building model. To validate the model, we cross-checked it with two benchmark models, iFroset and Random Forest. The final experimental results show that the model constructed in this paper can accurately detect outliers in traffic flow and the accuracy is better than that of the baseline model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. p. 3-9
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100, E-ISSN 1876-1119 ; 961
Keywords [en]
Anomaly detection, Traffic flow, XGBoost, Internet of things, Machine learning, Statistics, Data anomalies, Grid search, Machine-learning, Model-based OPC, Traffic flow analysis, Traffic prediction, Tree-based
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-24181DOI: 10.1007/978-981-19-6901-0_1Scopus ID: 2-s2.0-85144536702ISBN: 9789811969003 (print)OAI: oai:DiVA.org:bth-24181DiVA, id: diva2:1725258
Conference
12th International Conference on Computer Engineering and Networks, CENet 2022, Haikou, 4 November through 7 November 2022
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-01-10Bibliographically approved

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Sun, Bin

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  • apa
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